The number of direct and indirect fields

As per canton, province, partner etc.

The number of narrative records in form topics

The table shows the narrative fields with count of ’full’ and ‘empty’ fields.

• The form Tecnico (Medios de vida y formación técnico-profesional) has 182 and 1085 records in a total of 1267 records.

• The form VBG (Protección_VBG) has 78 and 430 records in a total of 508 records.

• The form Educacion (Acceso_a_educación) has 43 and 333 records in a total of 376 records.

• The form Poblacion (Manejo de la información y entrega directa de la información a la población) has 123 and 249 records in a total of 372 records.

• The form Coordinacion (Asistencia técnica para gestion de la informacion y coordinacion) has 43 and 215 records in a total of 258 records.

Please note that the following form topics are not included in the table above because they do not contain any narrative fields: Transporte humanitario, Protección general, Protección_Infancia, Protection_Otro, Asistencia técnica para Salud

Partners entering narrative data

Here, we look at the partners entering narrative data. The number of missing records (namely NAs) are excluded.

The number of ‘Reporting Partners’ and ‘Implementing Partners’ reporting narrative (multi-line text) data:

As we have seen previously, Not all Reporting and Implementing Partners record multi-line narrative textual data. For instance, the partner PMA has lots of Implementing Partners reporting for the different data types (as seen in above) but there are no narratives from them.

• 71% of the narrative records are entered by the implementing partner HIAS reported via ACNUR. Only 27% of the narrative records are entered by ACNUR itself.

• Also HIAS enters 26% of the narrative records via UNICEF.

• The rest of the “reporting partners” do not have any “implementing partners”, as it seems that they do the implementation: CARE, Dialogo Diverso, JRS Ecuador, Mision Scalabriniana, OIM, OPS/OMS, Plan Internacional, PNUD, UNESCO, UNFPA.

The number of cantons and provinces entering narrative data

Treemap plot showing canton and province reporting frequencies.

Word count

One issue with the nature of the questions is that they are only unique in a form. These questions can be distributed across multiple forms. The questions sharing the same name will have different meanings. For instance, the question “Cualitativo” from the form “Salud” should imply different thing than the question “Cualitativo” from the form “Protección_VBG”.

In order to solve this kind of problem:

• We can combine question with the form and also its folder label. There we can achieve a unique name for each question.

• Another thing to resolve this would be doing analysis to move the analysis up to form level. In this file, we did both, therefore the analysis shown as below:

Count of responses per topic/question (note that missing entries NAs are removed):

It’s also a good practice to see the number of questions. For example, one question has two responses, therefore they’re short.

Box plots are used to visualize the measure of spread showing the variability and dispersion of the data.

In the boxplot above, we see a number of things:

1. each individual black point in a group represents a “response” in the records, and it position indicates the value of word count;

2. left and right borders of the central rectangle (colored red) presents the first and third quartile values of IQR respectively;

3. the line in the middle of the rectangle indicates the median value;

4. the end of the lines streched from both right and left sides of the central rectangle point the maximum and minimum values;

5. the orange colored points show the outliers.

Some insights from this plot can be: TODO

The standard deviation is a single number statistics to show the measure of spread in data.

In the plot above, the box plot of form topics and response word counts based on the raw data, the outliers are shown in orange color. Outliers are the points placed outside the whiskers, which is the long line, of the boxplot.

The response word count distribution per form topic categorized by partner name:

The response word count distribution per form topic categorized by canton name:

A caveat: Reducing multiple values down to a single value should be avoided in the early stages of the analysis because reducing hides a lot e.g. a bar chart showing average the word count per partner. Some partners may write longer than others, because:

1. They actually write longer than other partners.

2. The questions they answered require short answers.

The Description field

Some questions have the description field giving extra details about the questions.

Do some questions with the extra description field have better response quality than the questions which do not have it?

Looking at the table containing form name, question, description and so on:

We see in the plot below that the response word counts per form and colored if a response has a description field or not. Having a description field or not is calculated as that a description field has a minimum one word.

The responses with the longest word counts are the ones with description. Nevertheless, it is not so easy to see a clear trend that there’s a correlation between response word count and description fields. Interestingly, the form topic Protección_VBG has no description fields at all in its form topics.

Analysis of Variance

TODO ANOVA

Trends

Correlation

The correlation between the word count of different fields in the ActivityInfo:

• 0.15% of the words in the response field have only 1 words. And those words were just not important (as they are TEST).

TODO

The regression lines

We can look at multiple continuous variables in our data.

• word count of response field: response.wc

• word count of question field: question.wc

• word count of description field: description.wc

Given this data, the model formula showing dependent and independent variables can be as follows:

$ response.wc question.wc $

$ response.wc question.wc + description.wc $

In that sense the word count of response field is the dependent variable and word count of question and word count of description fields are the independent variables in the regression.

Thus, we expect to have more word count in question and description fields to have a positive effect on the word count of response field.

Scatter plots help understand the characteristics of those variables. However, we miss a general understanding that is the trend line.

The gray area around the lines shows the confidence band at the 0.95 level. Although there’s a straight slope in the linear regression line, we cannot say that the trend line is robust because the confidence band representing the uncertainty in the estimate is wide.

Logistic regression

TODO In fact, it is called the binomial logistic regression. When one of the independent variables is dichotomous (having two categories), ...

Hypothesis testing

We do the hypothesis testing based on the assumptions we have.

$ H_0 $ : More word count in questions results in more word count in responses.

TODO

The most common words in all responses

The most common words in all questions

The most common words in all responses per form topic

Higher order n-grams

We can search the multi-type terms in the responses.

So the end result looks like this:

tf-idf values can also be calculated for bigrams, and visualized within each reporting/implementing partner, province/canton and so forth.